Learning in Fingerprints in .NET

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Learning in Fingerprints
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Table 14.4. AUC Obtained by the Fusion between Pairs of Feature Extraction Methods DCT DCT PCA LEM ICA GAB GLBP 0.89 0.88 0.89 0.88 0.85 0.92 PCA 0.88 0.84 0.88 0.86 0.83 0.91 LEM 0.89 0.88 0.89 0.88 0.86 0.92 ICA 0.88 0.86 0.88 0.83 0.81 0.90 GAB 0.85 0.83 0.86 0.81 0.68 0.85 GLBP 0.92 0.91 0.92 0.90 0.85 0.89
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r GAB: A feature vector is extracted by convolving the region with a bank of 16 Gabor lters [46] (four different wavelengths and four orientations: 0, 1/4 , 1/2 , 3/4 ). r GLBP: Invariant local binary patterns histogram with 10 bins (see [47] for details) are extracted after convolution with Gabor lters. Each feature vector has been normalized (through linear scaling) in the range [0, 1] and used to train a radial basis function SVM (with parameters Gamma = 1; C = 1000). Table 14.4 reports the AUC [48] obtained by combining through the sum rule the performance of the two classi ers located at each row column intersection. These results show the bene t of fusing classi ers trained on different features: the best stand-alone method (GLBP [46], as reported in the diagonal of Table 14.4) obtains an AUC of 0.89 while the fusion between LEM and GLBP raises AUC to 0.92. Since for minutiae ltering only the few regions classi ed as minutiae by the minutiae detector need to be processed, it is feasible to design complex and accurate systems, as the multiclassi er presented in this section, without compromising the whole ef ciency.
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Fingerprint matching is aimed at establishing if a pair of ngerprints, usually denoted as template T and input I, belongs to the same nger (i.e., match or not). The large intra-class variability in different impressions of the same nger (due to several perturbations such as displacement, rotation, distortions, different skin conditions, noise, etc.) makes ngerprint matching a dif cult problem. In this section we concentrate only on the methods based on learning; three main classes of approaches can be identi ed: r Minutiae-based matching r Ridge feature-based matching r Combination of matchers
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14.5 Matching
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Minutiae-Based Matching
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Most of the minutiae-based matching approaches address the problem as a point pattern matching problem [1]. Some researchers proposed methods based on evolutionary techniques where a learning stage is employed to optimize a given objective function for nding the best alignment between T and I: r Tan and Bhanu [49] used a traditional (and very time-consuming) Genetic Algorithm for nding the best alignment between two sets of minutiae. r Le et al. [50] employed the technique of fuzzy evolutionary programming. r Sheng et al. [51] developed a memetic ngerprint matching algorithm. In contrast to previous minutiae point pattern matching methods, this algorithm combines the use of a global search via a Genetic Algorithm with a local improvement operator used to prune the search. The tness of individual solutions is computed by combining (according to the product rule) the globally matched minutiae pairs with the result of the minutiae s local feature similarity. Additionally, an ef cient local matching operation for population initialization by examining local features of minutiae is proposed. Another class of works [52 54] formulates the ngerprint veri cation problem as a standard two-class pattern recognition problem (genuine versus impostor). During the training, for each pair of ngerprints A and B of a given training set, a feature vector c is produced from the matching between A and B; c is labeled as genuine if A and B belong to the same individual, as impostor otherwise. Finally, a generalpurpose classi er is trained to classify as genuine or impostor a generic feature vector. Once the system has been trained, an online veri cation can be simply performed by classifying the feature vector obtained by the matching between T and I. This formulation of the problem can be also conceived as a fusion (or combination) of partial scores where the learning phase is aimed at nding the optimal rules/weights for the fusion. r Jea and Govindaraju [52] perform the classi cation by means of a neural network trained on the following features extracted from the optimal minutiae alignment: (i) the number of mated minutiae n; (ii) the number of minutiae on T and I (nt, ni); (iii) two widely used expressions for similarity calculation: n2 2 n ni nt , ni+nt and (iv) another similarity score calculated by an heuristic method. r Jia et al. [53] use SVM trained on ve features: the number of the minutiae on T and I, the number of mated minutiae n, a minutiae s weight w, and the score of a standard method to compare ngerprints. Based on the observation that false minutiae are usually closer to each other than real ones, the authors conjecture that if the distances between a minutia and its neighboring minutiae are much smaller than the minutia in a false one, then they de ne the minutia s weight w according to the distance between the minutia and its nearest minutia. r Feng [54] extracts from the matching results a 17-dimensional feature vector and trains a SVM for the classi cation. The features used re ect the matching
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